Instructions to use MegaScience/Qwen2.5-3B-MegaScience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MegaScience/Qwen2.5-3B-MegaScience with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MegaScience/Qwen2.5-3B-MegaScience")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MegaScience/Qwen2.5-3B-MegaScience")
model = AutoModelForCausalLM.from_pretrained("MegaScience/Qwen2.5-3B-MegaScience", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MegaScience/Qwen2.5-3B-MegaScience with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MegaScience/Qwen2.5-3B-MegaScience"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MegaScience/Qwen2.5-3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MegaScience/Qwen2.5-3B-MegaScience

SGLang

How to use MegaScience/Qwen2.5-3B-MegaScience with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MegaScience/Qwen2.5-3B-MegaScience" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MegaScience/Qwen2.5-3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MegaScience/Qwen2.5-3B-MegaScience" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MegaScience/Qwen2.5-3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MegaScience/Qwen2.5-3B-MegaScience with Docker Model Runner:
```
docker model run hf.co/MegaScience/Qwen2.5-3B-MegaScience
```

Qwen2.5-3B-MegaScience

File size: 2,706 Bytes

---
base_model:
- Qwen/Qwen2.5-3B
datasets:
- MegaScience/MegaScience
language:
- en
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
library_name: transformers
---

# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://huggingface.co/papers/2507.16812)

This repository contains the `Qwen2.5-3B-MegaScience` model, one of the models trained as part of the MegaScience project.

For the official code, data processing pipeline, and evaluation system, please refer to the [MegaScience GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).

## Qwen2.5-3B-MegaScience

### Usage

You can use this model with the Hugging Face `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "MegaScience/Qwen2.5-3B-MegaScience"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example text generation
prompt = "The capital of France is"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt")

generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=20)
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
```

### Training Recipe

- **LR**: 5e-6
- **LR Schedule**: Cosine
- **Batch Size**: 512
- **Max Length**: 4,096
- **Warm Up Ratio**: 0.05
- **Epochs**: 3

### Evaluation Results

<div style="display: flex; justify-content: left; gap: 20px;">
<img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F616bfc2b40e2f69baa1c7add%2FabIVZ2XB9D-o-TCyvOkDE.png%26quot%3B%3C%2Fspan%3E alt="Data Pipeline" style="width:80%;">
</div>

<div style="display: flex; justify-content: left; gap: 20px;">
<img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F616bfc2b40e2f69baa1c7add%2FxFTJ7nevc3S4UYJxUS7ue.png%26quot%3B%3C%2Fspan%3E alt="Data Pipeline" style="width:80%;">
</div>

### More about MegaScience

<div style="display: flex; justify-content: left; gap: 20px;">
<img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F616bfc2b40e2f69baa1c7add%2FVogIpBbjfNxXFP9DfVMms.png%26quot%3B%3C%2Fspan%3E alt="Data Pipeline" style="width:100%;">
</div>

## Citation

Check out our [paper](https://arxiv.org/abs/2507.16812) for more details. If you use our dataset or find our work useful, please cite

```
@article{fan2025megascience,
  title={MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning},
  author={Fan, Run-Ze and Wang, Zengzhi and Liu, Pengfei},
  year={2025},
  journal={arXiv preprint arXiv:2507.16812},
  url={https://arxiv.org/abs/2507.16812}
}
```